Structural variant detection in cancer genomes: computational challenges and perspectives for precision oncology

Abstract Cancer is generally characterized by acquired genomic aberrations in a broad spectrum of types and sizes, ranging from single nucleotide variants to structural variants (SVs). At least 30% of cancers have a known pathogenic SV used in diagnosis or treatment stratification. However, research...

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Autores principales:	Ianthe A. E. M. van Belzen, Alexander Schönhuth, Patrick Kemmeren, Jayne Y. Hehir-Kwa
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
Acceso en línea:	https://doaj.org/article/54120bba32934727835d00e52dbb27cf
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spelling	oai:doaj.org-article:54120bba32934727835d00e52dbb27cf2021-12-02T14:41:37ZStructural variant detection in cancer genomes: computational challenges and perspectives for precision oncology10.1038/s41698-021-00155-62397-768Xhttps://doaj.org/article/54120bba32934727835d00e52dbb27cf2021-03-01T00:00:00Zhttps://doi.org/10.1038/s41698-021-00155-6https://doaj.org/toc/2397-768XAbstract Cancer is generally characterized by acquired genomic aberrations in a broad spectrum of types and sizes, ranging from single nucleotide variants to structural variants (SVs). At least 30% of cancers have a known pathogenic SV used in diagnosis or treatment stratification. However, research into the role of SVs in cancer has been limited due to difficulties in detection. Biological and computational challenges confound SV detection in cancer samples, including intratumor heterogeneity, polyploidy, and distinguishing tumor-specific SVs from germline and somatic variants present in healthy cells. Classification of tumor-specific SVs is challenging due to inconsistencies in detected breakpoints, derived variant types and biological complexity of some rearrangements. Full-spectrum SV detection with high recall and precision requires integration of multiple algorithms and sequencing technologies to rescue variants that are difficult to resolve through individual methods. Here, we explore current strategies for integrating SV callsets and to enable the use of tumor-specific SVs in precision oncology.Ianthe A. E. M. van BelzenAlexander SchönhuthPatrick KemmerenJayne Y. Hehir-KwaNature PortfolioarticleNeoplasms. Tumors. Oncology. Including cancer and carcinogensRC254-282ENnpj Precision Oncology, Vol 5, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
spellingShingle	Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282 Ianthe A. E. M. van Belzen Alexander Schönhuth Patrick Kemmeren Jayne Y. Hehir-Kwa Structural variant detection in cancer genomes: computational challenges and perspectives for precision oncology
description	Abstract Cancer is generally characterized by acquired genomic aberrations in a broad spectrum of types and sizes, ranging from single nucleotide variants to structural variants (SVs). At least 30% of cancers have a known pathogenic SV used in diagnosis or treatment stratification. However, research into the role of SVs in cancer has been limited due to difficulties in detection. Biological and computational challenges confound SV detection in cancer samples, including intratumor heterogeneity, polyploidy, and distinguishing tumor-specific SVs from germline and somatic variants present in healthy cells. Classification of tumor-specific SVs is challenging due to inconsistencies in detected breakpoints, derived variant types and biological complexity of some rearrangements. Full-spectrum SV detection with high recall and precision requires integration of multiple algorithms and sequencing technologies to rescue variants that are difficult to resolve through individual methods. Here, we explore current strategies for integrating SV callsets and to enable the use of tumor-specific SVs in precision oncology.
format	article
author	Ianthe A. E. M. van Belzen Alexander Schönhuth Patrick Kemmeren Jayne Y. Hehir-Kwa
author_facet	Ianthe A. E. M. van Belzen Alexander Schönhuth Patrick Kemmeren Jayne Y. Hehir-Kwa
author_sort	Ianthe A. E. M. van Belzen
title	Structural variant detection in cancer genomes: computational challenges and perspectives for precision oncology
title_short	Structural variant detection in cancer genomes: computational challenges and perspectives for precision oncology
title_full	Structural variant detection in cancer genomes: computational challenges and perspectives for precision oncology
title_fullStr	Structural variant detection in cancer genomes: computational challenges and perspectives for precision oncology
title_full_unstemmed	Structural variant detection in cancer genomes: computational challenges and perspectives for precision oncology
title_sort	structural variant detection in cancer genomes: computational challenges and perspectives for precision oncology
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/54120bba32934727835d00e52dbb27cf
work_keys_str_mv	AT iantheaemvanbelzen structuralvariantdetectionincancergenomescomputationalchallengesandperspectivesforprecisiononcology AT alexanderschonhuth structuralvariantdetectionincancergenomescomputationalchallengesandperspectivesforprecisiononcology AT patrickkemmeren structuralvariantdetectionincancergenomescomputationalchallengesandperspectivesforprecisiononcology AT jayneyhehirkwa structuralvariantdetectionincancergenomescomputationalchallengesandperspectivesforprecisiononcology
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Structural variant detection in cancer genomes: computational challenges and perspectives for precision oncology

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